XGBoost CO2 Concentration Prediction via Global Sensitivity Analysis
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Solution Overview
Problem
Current methods for analyzing the spatiotemporal distribution of carbon dioxide concentrations in regions with satellite observation gaps face challenges due to low accuracy and complexity, particularly in considering multiple environmental and anthropogenic factors, which limits the ability to quantify their influence on atmospheric carbon dioxide levels.
Innovation Solution
A machine-learning-based method using the eXtreme Gradient Boosting tree (XGBoost) algorithm constructs a carbon dioxide spatiotemporal distribution simulation model, combined with global sensitivity analysis via the Sobol method, to classify and quantify the importance of environmental factors such as ground coverage, climate, and anthropogenic emissions, filling data gaps and providing accurate regional CO2 concentration predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to simulate CO2 spatiotemporal distribution, then prediction accuracy is improved, but model complexity increases
Solution Approach 1:
The patent segments the complex machine learning model into multiple independent components: feature engineering module, model training module, and prediction module. Each component handles specific tasks independently, making the overall system more manageable and easier to optimize without sacrificing prediction accuracy.
Solution Approach 2:
The patent implements dynamic model selection and hyperparameter tuning based on the specific characteristics of the input data. The system automatically adjusts model complexity dynamically, selecting simpler models when sufficient and more complex models only when necessary, thereby optimizing the balance between accuracy and complexity.
2Measurement precision
If multiple environmental and anthropogenic factors are considered in the model, then prediction accuracy is improved, but computational cost increases
Solution Approach 1:
The patent applies partial action by selectively incorporating only the most relevant environmental and anthropogenic factors based on their contribution to prediction accuracy. The system performs feature importance analysis and includes only those factors that provide significant predictive value, avoiding the computational overhead of processing all possible factors.
Solution Approach 2:
The patent dynamically adjusts the number and types of factors considered based on the specific spatiotemporal context. In regions or time periods where certain factors are known to be less influential, the model reduces their weight or excludes them entirely, thereby reducing computational cost while maintaining accuracy where it matters most.
3Measurement precision
If satellite observation data is used, then measurement accuracy is improved, but data coverage is reduced due to cloud and aerosol gaps
Solution Approach 1:
The patent introduces machine learning models as an intermediary between satellite observations and final CO2 concentration estimates. The model learns from available satellite data and uses environmental and anthropogenic factors as intermediate variables to infer CO2 concentrations in areas where satellite data is unavailable due to clouds or aerosols, thereby extending coverage while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary training of the machine learning model using all available satellite observation data under clear sky conditions. This preliminary action creates a robust model that can then be applied to predict CO2 concentrations in areas where satellite data is unavailable, effectively preparing the system in advance to handle data gaps.
Data Source
AI summary
The disclosure provides a method of analyzing an influence factor for predicting a carbon dioxide concentration of any spatiotemporal position. Firstly, an atmospheric carbon dioxide spatiotemporal distribution simulation method is proposed. This simulation method constructs a simulation model simulating carbon dioxide concentration distribution of any position of a region based on machine learning algorithm in combination with carbon dioxide data of satellite observation and corresponding environmental factors; next, by use of a global sensitivity analysis method, quantitative evaluation on the importance of multiple influence factors for regional carbon dioxide distribution is achieved.